Advertisement

Journal of Intelligent & Robotic Systems

, Volume 89, Issue 3–4, pp 319–342 | Cite as

Characterisation of Grasp Quality Metrics

  • Carlos RubertEmail author
  • Beatriz León
  • Antonio Morales
  • Joaquín Sancho-Bru
Article

Abstract

Robot grasp quality metrics are used to evaluate, compare and select robotic grasp configurations. Many of them have been proposed based on a diversity of underlying principles and to assess different aspects of the grasp configurations. As a consequence, some of them provide similar information but other can provide completely different assessments. Combinations of metrics have been proposed in order to provide global indexes, but these attempts have shown the difficulties of merging metrics with different numerical ranges and even physical units. All these studies have raised the need of a deeper knowledge in order to determine independent grasp quality metrics which enable a global assessment of a grasp, and a way to combine them. This paper presents an exhaustive study in order to provide numerical evidence for these issues. Ten quality metrics are used to evaluate a set of grasps planned by a simulator for 7 different robot hands over a set of 126 object models. Three statistical analysis, namely, variability, correlation and sensitivity, are performed over this extensive database. Results and graphs presented allow to set practical thresholds for each quality metric, select independent metrics, and determine the robustness of each metric,providing a reliability indicator under pose uncertainty. The results from this paper are intended to serve as guidance for practical use of quality metrics by researchers on grasp planning algorithms.

Keywords

Grasp planning Multifingered hands Quality metrics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

This research was partly supported by Ministerio de Educación, Ciencia y Tecnología (Grant No. R31 - 2008 - 000 - 10062 - 0), by Ministerio de Ciencia e Innovación (DPI2011 - 27846), by Ministerio de Economía y Competitividad (DPI2014 - 60635 - R) by Generalitat Valenciana (PROMETEO/2009/052, PROMETEOII/2014/028 ) and by Fundació Caixa Castelló-Bancaixa (P1 - 1B2011 - 54 and PI - 1B2011 - 25).

References

  1. 1.
    Aleotti, A., Caselli, S.: Grasp recognition in virtual reality for robot pregrasp planning by demonstration. Proc. - IEEE Int. Conf. Robot. Autom. 2006, 2801 (2006)Google Scholar
  2. 2.
    Balasubramanian, R., Xu, L., Brook, P.D., Smith, J.R., Matsuoka, Y.: Physical human interactive guidance: Identifying grasping principles from human-planned grasps. IEEE Trans. Robot. 28 (4), 899–910 (2012)CrossRefGoogle Scholar
  3. 3.
    Barrett Technology Inc.: BarrettHand. http://www.barrett.com/robot/products-hand.htm
  4. 4.
    Bicchi, A.: Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity. IEEE Trans. Robot. Autom. 16(6), 652–662 (2000)CrossRefGoogle Scholar
  5. 5.
    Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasps synthesis - a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)CrossRefGoogle Scholar
  6. 6.
    Boivin, E., Sharf, I., Doyon, M.: Optimum Grasp of Planar and Revolute Objects with Gripper Geometry Constraints. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 326–332 (2004)Google Scholar
  7. 7.
    Chinellato, E., Morales, A., Fisher, R., del Pobil, A.: Visual quality measures for characterizing planar robot grasps. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(1), 30–41 (2005)CrossRefGoogle Scholar
  8. 8.
    Deutsche Forschungsgemeinschaft: KIT ObjectModels Web Database. Object Models of Household Items. http://i61p109.ira.uka.de/ObjectModelsWebUI/index.php?section=home
  9. 9.
  10. 10.
    Diankov, R.: Automated construction of robotic manipulation programs. Ph.D. thesis, Carnegie Mellon University Robotics Institute (2010)Google Scholar
  11. 11.
    Diankov, R., Kuffner, J.: Openrave: A planning architecture for autonomous robotics. Tech. Rep. CMU-RI-TR-08-34, Robotics Institute, Pittsburgh, PA (2008)Google Scholar
  12. 12.
    Ding, D., Lee, Y.H., Wang, S.: Computation of 3-d form-closure grasps. IEEE Trans. Robot. Autom. 17(4), 515–522 (2001)CrossRefGoogle Scholar
  13. 13.
    Ferrari, C., Canny, J.: Planning optimal grasps. In: Proceedings 1992 IEEE International Conference on Robotics and Automation pp. 2290–2295 (1992)Google Scholar
  14. 14.
    Fieller, E.C., Hartley, H.O., Pearson, E.S.: Tests for rank correlation coefficients. I. Biometrika 44(3-4), 470–481 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hang, K., Pokorny, F.T., Kragic, D.: Friction Coefficients and Grasp Synthesis. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. 1 (2013)Google Scholar
  16. 16.
    Hester, R., Cetin, M., Kapoor, C., Tesar, D.: A Criteria-Based Approach to Grasp Synthesis Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference On, vol. 2, pp. 1255–1260 (1999)Google Scholar
  17. 17.
    Kim, B.H., Oh, S.R., Yi, B.J., Suh, I.H.: Optimal Grasping Based on Non-Dimensionalized Performance Indices. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, vol. 2, pp. 949–956 (2001)Google Scholar
  18. 18.
    Kim, J.O., Khosla, P.: Dexterity measures for design and control of manipulators. Proceedings IROS Workshop on Intelligent Robots and Systems pp. 758–763 (1991)Google Scholar
  19. 19.
    Kirkpatrick, D.G., Mishra, B., Yap, C.K.: Quantitative Steinitz’s Theorems with Applications to Multifingered Grasping. In: Proceedings of the Twenty-Second Annual ACM Symposium on Theory of Computing, STOC ’90, pp. 341–351. ACM, New York (1990)CrossRefGoogle Scholar
  20. 20.
    León, B., Morales, A., Sancho-Bru, J.: From Robot to Human Grasping Simulation, Cognitive Systems Monographs, vol. 19 Springer International Publishing (2013)Google Scholar
  21. 21.
    Leon, B., Rubert, C., Sancho-Bru, J., Morales, A.: Evaluation of Prosthetic Hands Prehension Using Grasp Quality Measures. In: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On, pp. 3501–3506 (2013)Google Scholar
  22. 22.
    Leon, B., Rubert, C., Sancho-Bru, J., Morales, A.: Characterization of Grasp Quality Measures for Evaluating Robotic Hands Prehension. In: Robotics and Automation (ICRA), 2014 IEEE International Conference On, pp. 3688–3693 (2014)Google Scholar
  23. 23.
    León, B., Sancho-Bru, J., Jarque-Bou, N., Morales, A., Roa, M.: Evaluation of human prehension using grasp quality measures. International Journal of Advanced Robotic Systems (2012)Google Scholar
  24. 24.
    Li, Z., Sastry, S.: Task-oriented optimal grasping by multifingered robot hands. IEEE J. Robot. Autom. 4(1), 32–44 (1987)CrossRefGoogle Scholar
  25. 25.
    Liegeois, A.: Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Trans. Syst. Man Cybern. 7(12), 842–868 (1977)zbMATHGoogle Scholar
  26. 26.
    Miller, A.T., Allen, P.K.: Examples of 3D Grasp Quality Computations Proceedingsof the IEEE International Conference on Robotics and Automation, vol. 2, pp. 1240–1246 (1999)Google Scholar
  27. 27.
    Mirtich, B., Canny, J.: Easily Computable Optimum Grasps in 2-D and 3-D. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 739–747 (1994)Google Scholar
  28. 28.
    Mishra, B.: Grasp Metrics: OptiMality and Complexity Proceedings of the Workshop on Algorithmic Foundations of Robotics, WAFR, pp. 137–165. A. K. Peters, Ltd., Natick (1995)Google Scholar
  29. 29.
  30. 30.
    Ponce, J., Sullivan, S., Sudsang, A., Boissonnat, J.D., Merlet, J.P.: On computing four-finger equilibrium and force-closure grasps of polyhedral objects. Int. J. Robot. Res. 16(1), 11–35 (1997)CrossRefzbMATHGoogle Scholar
  31. 31.
    Roa, M.A., Suárez, R.: Grasp quality measures: review and performance. Auton. Robot. pp. 1–24 (2014)Google Scholar
  32. 32.
    Rombokas, E., Brook, P., Smith, J.R., Matsuoka, Y.: Biologically inspired grasp planning using only orthogonal approach angles. In: Biomedical Robotics and Biomechatronics (Biorob), 2012 4Th IEEE RAS & EMBS International Conference On, pp. 1656–1661 (2012)Google Scholar
  33. 33.
    Rubert, C. Openhand grasp database viewer. https://github.com/Cescuder/OpenHand-Viewer
  34. 34.
    Rubert, C., Leon, B., Morales, A.: Grasp quality metrics for robot hands benchmarking. In: Humanoid Robots, 2014 IEEE/RSJ International Conference On (2014)Google Scholar
  35. 35.
    Rubert, C., Morales, A.: Comparison between grasp quality metrics and the anthropomorphism index for the evaluation of artificial hands. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1352–1357 (2016). doi: 10.1109/BIOROB.2016.7523820
  36. 36.
    Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3d object grasp synthesis algorithms. Robot. Auton. Syst. 60(3), 326–336 (2012)CrossRefGoogle Scholar
  37. 37.
    Salisbury, J.K., Craig, J.J.: Articulated hands: Force control and kinematic issues. Int. J. Robot. Res. 1(1), 4–17 (1982)CrossRefGoogle Scholar
  38. 38.
    Savescu, A.V., Latash, M.L., Zatsiorsky, V.M.: A technique to determine friction at the fingertips. J. Appl. Biomech. 24(1), 43–50 (2008)CrossRefGoogle Scholar
  39. 39.
    Schunk GmbH & Co. KG: Schunk SDL HandGoogle Scholar
  40. 40.
    SciPy Developers: Scipy. http://scipy.org/
  41. 41.
    Shadow Robot Company: Shadow Hand. http://www.shadowrobot.com/products/dexterous-hand/
  42. 42.
    Shimoga, K.B.: Robot grasp synthesis algorithms: a survey. Int. J. Robot. Res. 15(3), 230–266 (1996)CrossRefGoogle Scholar
  43. 43.
    Weisz, J., Allen, P.K.: Pose Error Robust Grasping from Contact Wrench Space Metrics. In: IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 557–562 (2012)Google Scholar
  44. 44.
  45. 45.
    Xiong, C., Li, Y., Ding, H., Xiong, Y.L.: On the dynamic stability of grasping. I. J. Robot. Res. 18(9), 951–958 (1999)CrossRefGoogle Scholar
  46. 46.
    Yale OpenHand Project: Model T. http://www.eng.yale.edu/grablab/openhand/
  47. 47.
    Zheng, Y., Qian, W.H.: Coping with the grasping uncertainties in force-closure analysis. p. 311–327. SAGE Publications, The International Journal of Robotics Research (2005)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Robotic Intelligence Laboratory at the Department of Computer Science and EngineeringUniversitat Jaume ICastellónSpain
  2. 2.Group of Biomechanics and Ergonomics at the Department of Mechanical Engineering and ConstructionUniversitat Jaume ICastellónSpain

Personalised recommendations